Method and device for monitoring a status of at least one wind turbine and computer program product

Abstract

The invention relates to a method (200) for monitoring a status of at least one wind turbine. The method (200) comprises: detecting first measurement signals via one or more sensors (210), wherein the first measurement signals provide one or more parameters relating to at least one rotor blade of the at least one wind turbine in a normal status; training a trainable algorithm based on the first measurement signals of the normal status (220); detecting second measurement signals via the one or more sensors (230); and recognising an undetermined anomaly via the trainable algorithm trained in the normal status, if a current status of the wind turbine, determined based on the second measurement signals, deviates from the normal status (240).

1 . A method for monitoring a status of at least one wind turbine, comprising:
 detecting first measurement signals via one or more sensors, wherein the first measurement signals provide one or more parameters relating to at least one rotor blade of the at least one wind turbine in a normal status;   training a trainable algorithm based on the first measurement signals of the normal status;   detecting second measurement signals via the one or more sensors; and   recognizing an undetermined anomaly via the trainable algorithm trained in the normal status, if a current status of the wind turbine, determined based on the second measurement signals, deviates from the normal status.   
     
     
         2 . The method according to  claim 1 , wherein the normal status is depicted using the first measurement signals, and the current status is depicted using the second measurement signals, and wherein the undetermined anomaly is recognized by comparing the normal status with the current status. 
     
     
         3 . The method according to  claim 1 , wherein the trained trainable algorithm does not comprise any predetermined anomalies. 
     
     
         4 . The method according  claim 1 , further comprising completing the trainable algorithm with the recognized undetermined anomaly. 
     
     
         5 . The method according to  claim 4 , wherein, upon a repeated occurrence of substantially the same undetermined anomaly, the trainable algorithm recognizes the undetermined anomaly again. 
     
     
         6 . The method according to  claim 1 , wherein the training of the trainable algorithm is performed in an undamaged status of the wind turbine. 
     
     
         7 . The method according to  claim 1 , wherein the first measurement signals and the second measurement signals are optical signals. 
     
     
         8 . The method according to  claim 1 , wherein the undetermined anomaly is recognized when the deviation of the current status from the normal status is greater than a reference deviation. 
     
     
         9 . The method according to  claim 8 , wherein an undetermined anomaly is not recognized when the deviation of the current status from the normal status is less than the reference deviation. 
     
     
         10 . The method according to  claim 1 , wherein the trainable algorithm is provided by a neural network. 
     
     
         11 . The method according to  claim 1 , further comprising:
 outputting a message relating to the recognized undetermined anomaly.   
     
     
         12 . The method according to  claim 1 , further comprising:
 carrying out a plausibility check of the recognized undetermined anomaly.   
     
     
         13 . The method according to  claim 1 , wherein the one or more parameters is or are selected from the group comprising the natural frequency of the rotor blade, a rotor speed, a supplied energy, a temperature, an angle of attack of the rotor blade, a pitch angle and a speed of incidence. 
     
     
         14 . The method according to  claim 1 , wherein the at least one wind turbine is a plurality of wind turbines. 
     
     
         15 . A device for monitoring a status of at least one wind turbine, comprising:
 one or more sensors for detecting first measurement signals, wherein the first measurement signals indicate one or more parameters relating to at least one rotor blade of the wind turbine in a normal status; and   an electronic device including a trainable algorithm and configured to   train the trainable algorithm based on the first measurement signals of the normal status,   receive second measurement signals detected via the one or more sensors; and   recognize an undetermined anomaly, if a current status of the wind turbine, determined based on the second measurement signals, deviates from the normal status.   
     
     
         16 . A computer program product, comprising a trainable algorithm which is arranged to be trained based on first measurement signals of a normal status of a wind turbine, and to recognize an undetermined anomaly, if a current status, determined based on the second measurement signals, deviates from the normal status.